Catalogue of Tools & Metrics for Trustworthy AI

These tools and metrics are designed to help AI actors develop and use trustworthy AI systems and applications that respect human rights and are fair, transparent, explainable, robust, secure and safe.

Fairness

Clear all

Scope

SUBMIT A METRIC

If you have a tool that you think should be featured in the Catalogue of AI Tools & Metrics, we would love to hear from you!

SUBMIT
This page includes technical metrics and methodologies for measuring and evaluating AI trustworthiness and AI risks. These metrics are often represented through mathematical formulas that assess the technical requirements for achieving trustworthy AI in a particular context. They can help to ensure that a system is fair, accurate, explainable, transparent, robust, safe, or secure.
Objective Fairness

If a model systematically makes errors disproportionately for patients in the protected group, it is likely to lead to unequal outcomes. Equal performance refers to the assurance that a model is equally accurate for patients in the protec...

Objectives:


We propose a criterion for discrimination against a specified sensitive attribute in supervised learning, where the goal is to predict some target based on available features. Assuming data about the predictor, target, and membership in the protected group...

Objectives:


We study fairness in classification, where individuals are classified, e.g., admitted to a university, and the goal is to prevent discrimination against individuals based on their membership in some group, while maintaining utility for the classifier (the ...

Objectives:


Hellinger distance

The Hellinger distance (sometimes called the Jeffreys distance) is a metric in the space of probability distributions. The Hellinger distance can be used to quantify the degree of similarity between two probability ...

Objectives:


This paper proposes a new bias evaluation metric – Gender-based Illicit Proximity Estimate (GIPE), which measures the extent of undue proximity in word vectors resulting from the presence of gender-based predilections. Experiments based on a suite of...

Objectives:


The demographic disparity metric (DD) determines whether a facet has a larger proportion of the rejected outcomes in the dataset than of the accepted outcomes. In the binary case where there are two facets, men and women for example, that constitute the dat...

Objectives:


RADio introduces a rank-aware Jensen Shannon (JS) divergence. This combination accounts for (i) a user’s decreasing propensity to observe items further down a list and (ii) full distributional shifts as opposed to point estimates.

Objectives:


Contextual Outlier INterpretation (COIN) is a method designed to explain the abnormality of existing outliers spotted by detectors. The interpretability for an outlier is achieved from three aspects: outlierness score, att that contribute to the abnormality, a...

Objectives:


Given an input data sample, LEMNA generates a small set of interpretable features to explain how the input sample is classified. The core idea is to approximate a local area of the complex deep learning decision boundary using a simple interpretable model. The...

Objectives:


SHAP (SHapley Additive exPlanations) assigns each feature an importance value for a particular prediction. Its novel components include: (1) the identification of a new class of additive feature importance measures, and (2) theoretical results showing there is...

Objectives:


LIME is a novel explanation technique that explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction.

Objectives:


Following the VIC framework, our proposed ShapleyVIC extends the widely used Shapley-based variable importance measures beyond final models for a comprehensive assessment and has important practical implications.

Objectives:


Ideally we would like to obtain a more complete understanding of variable importance for the set of models that predict almost equally well. This set of almost-equally-accurate predictive models is called the Rashomon set; it is the set of models with training...

Objectives:


The Banzhaf power index is a power index defined by the probability of changing an outcome of a vote where voting rights are not necessarily equally divided among the voters. Data Banzhaf uses this notion to measure data points' "voting powers" towards algorit...

Objectives:


In a cooperative game, there are n players D = {1,...,n} and a score function v : 2[n] → R assigns a reward to each of 2 n subsets of players: v(S) is the reward if the players in subset S ⊆ D cooperate. We view the supervised machine learning problem as a coo...

Objectives:


The PGC metric compares the top-K ranking of features importance drawn from the entire dataset with the top-K ranking induced from specific subgroups of predictions. It can be applied to both categorical and regression problems, being useful for quantifying...

Objectives:


The metric GFIS is based on the concept of entropy. More precisely on the entropy of the normalized features measure, which represents the concentration of information within a set of features. Lower entropy values indicate that the majority of the explanat...

Objectives:


Machine learning models, at the core of AI applications,  typically achieve a high accuracy at the expense of an insufficient explainability. Moreover, according to the proposed regulations,  AI applications based on machine learning must be "trus...

Objectives:


In statistics, the Kendall rank correlation coefficient, commonly referred to as Kendall's τ coefficient, is a statistic used to measure the ordinal association between two measured quantities. A τ test is a non-parametric hypothesis test for statistical de...

Objectives:


In the field of health, equal patient outcomes refers to the assurance that protected groups have equal benefit in terms of patient outcomes from the deployment of machine-learning models. A weak form of equal outcomes is ensuring that both the protect...

Objectives:


catalogue Logos

Disclaimer: The tools and metrics featured herein are solely those of the originating authors and are not vetted or endorsed by the OECD or its member countries. The Organisation cannot be held responsible for possible issues resulting from the posting of links to third parties' tools and metrics on this catalogue. More on the methodology can be found at https://oecd.ai/catalogue/faq.